Many process-mining vendors claim they offer AI-powered process mining software. However, many clients don’t know how AI features are integrated into process-mining software, making it difficult to explain the benefits and use cases for integrating AI with process mining to business leaders.
This article explains how AI can be used to enhance process mining and what applications could benefit from AI-enabled mining.
Is AI used in process mining?
Although most process mining tools don’t leverage AI, there are some vendors that integrate AI features like machine-learning or deep learning to automate the collection, discovery, and visualization of process data. AI-enabled mining can also enhance features of other applications, such as predictive analytics and digital twins.
These functionalities are the most useful for AI implementation:
- Data collection, cleaning, and preparation
- Process categorization
- Analyzing and identifying process problems
Also read: What is Explainable AI and How to Build Trust in AI Models
1. Automated process discovery
Automated process discovery is a term that refers to vendors offering process discovery tools that use AI technology and algorithms to automatically discover workflows and models. Utilizing computer vision, automated process discovery can help identify human interaction.
2. Predictive process mining
The capabilities of process mining extend beyond the ability to map as-is processes and detect bottlenecks. Companies want to predict when and how likely they are to experience the next inefficiency and then take steps to prevent them from becoming costly. Vendors are now developing predictive and prospective process mining tools that either use supervised or unsupervised machine learning algorithms, or combine predictive analytics capabilities.
3. AI-powered root cause analysis
The root causes of identified errors and bottlenecks cannot be determined by process mining. Process mining vendors use machine learning algorithms such as anomaly detection to overcome this limitation. These algorithms often calculate correlations and then split the data according to user-friendly charts.
4. Digital twins
Digital twins use AI tools to gather real-time information about products and processes and create virtual models. By integrating digital twins into their process mining solutions, vendors can enable businesses to model and test scenarios.
Process mining tools can also use explainable AI in order to map compliance processes and identify risks in real-time. This feature can be integrated into a digital twin to improve the model’s predictions regarding compliance and risk.
Also read: How Do KPIs Measure the Success of an AI Project?
Recommendation for business
Despite increasing interest, AI applications for process mining remain limited. This is because businesses continue to struggle with data integration problems, which prevents them from leveraging AI capabilities that rely on extensive quality data.
By storing their data in a cloud or data warehouse, companies can reduce the integration problem. Companies can make it easier to consolidate their data, and then start process mining by using warehouse automation software.
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